竞猜网站建设,广州seo网站服务公司,wordpress二次元动漫主题,企业注册官网入口算法基本流程如下#xff1a;
1. 采集音乐库 2. 音乐指纹采集 3. 采用局部最大值作为特征点 峰值查找#xff1a;signal.find_peaks(spectrum, prominence0, distance200)
prominence#xff1a;显著性阈值#xff0c;峰值点左右延伸一条直线不超过比当前峰值高的点
1. 采集音乐库 2. 音乐指纹采集 3. 采用局部最大值作为特征点 峰值查找signal.find_peaks(spectrum, prominence0, distance200)
prominence显著性阈值峰值点左右延伸一条直线不超过比当前峰值高的点左右两段最小值中的较大值与峰值之间的差值作为显著性值distance水平距离峰值之间水平距离阈值。
a np.array([2, 3, 1, 5, 4, 7, 2, 6, 2])
peak_idx, props signal.find_peaks(a, distance1, prominence0, height3)
peak_idx, props
(array([1, 3, 5, 7], dtypeint64),{peak_heights: array([3., 5., 7., 6.]),prominences: array([1., 1., 5., 4.]),left_bases: array([0, 2, 2, 6], dtypeint64),right_bases: array([2, 4, 6, 8], dtypeint64)})符合条件的峰值点索引为[1,3,5,7],对应值为[3,5,7,6]
1. 峰值3的显著性从峰值3向左右分别延长一条直线直到最后一个不超过当前峰值的点得到两段线段左边线段最小值称为left_base右边线段最小值称为right_base3向左延伸到2索引0向右延伸到2索引2显著性值为3-max(a[0], a[2])1
2. 峰值5的显著性5向左延伸到1索引2向右延伸到4索引4,显著性值为5-max(a[2], a[4])1
3. 峰值7的显著性7向左延伸到1索引2向右延伸到2索引6,显著性值为7-max(a[2], a[6])5
2. 峰值6的显著性6向左延伸到2索引6向右延伸到2索引8,显著性值为6-max(a[6], a[8])4 4. 将临近的特征点进行组合形成特征点对 5. 对每个特征点对进行hash编码 编码过程将f1和f2进行10bit量化其余bit用来存储时间偏移合集形成32bit的hash码
Hash f1|f210|diff_t20存储信息t1Hash 具体步骤如下
傅里叶变换得到频谱每帧选取n_peaks个极大值点(基于显著性的峰值查找)—(i, freq)所有特征点追加到列表;每个极值点在其附近邻域如i:i100内生成一定数量特征对附近邻域满足一定条件如diffother_time-cur_time,diff1diff10将特征对编码成Hash值(32位)diff20 | other_time10 |cur_timeHash值存入数据库Hash—ref_i, song_id提取待检索音频的Hash值查找其Hash值在数据库中哪些音乐中出现song_id—Hash, ref_i, test_i统计同一song_id中相对时间偏移test_i-ref_i出现最多的次数统计所有song_id中相对偏移最多次数最大的索引即得到检索的song_id。
具体实现如下
import numpy as np
import librosa
from scipy import signal
import pickle
import os
fix_rate 16000
win_length_seconds 0.5
frequency_bits 10
num_peaks 15# 构造歌曲名与歌曲id之间的映射字典
def song_collect(base_path):index 0dic_idx2song {}for roots, dirs, files in os.walk(base_path):for file in files:if file.endswith((.mp3, .wav)):file_song os.path.join(roots, file)dic_idx2song[index] file_songindex 1return dic_idx2song# 提取局部最大特征点
def collect_map(y, fs, win_length_seconds0.5, num_peaks15):win_length int(win_length_seconds * fs)hop_length int(win_length // 2)n_fft int(2**np.ceil(np.log2(win_length)))S librosa.stft(y, n_fftn_fft, win_lengthwin_length, hop_lengthhop_length)S np.abs(S) # 获取频谱图D, T np.shape(S)constellation_map [] for i in range(T):spectrum S[:, i]peaks_index, props signal.find_peaks(spectrum, prominence0, distance200)# 根据显著性进行排序n_peaks min(num_peaks, len(peaks_index))largest_peaks_index np.argpartition(props[prominences], -n_peaks)[-n_peaks:]for peak_index in peaks_index[largest_peaks_index]:frequency fs / n_fft * peak_index# 保存局部最大值点的时-频信息constellation_map.append([i, frequency])return constellation_map# 进行Hash编码
def create_hash(constellation_map, fs, frequency_bits10, song_idNone):upper_frequency fs / 2hashes {}for idx, (time, freq) in enumerate(constellation_map):for other_time, other_freq in constellation_map[idx: idx 100]: # 从邻近的100个点中找点对diff int(other_time - time)if diff 1 or diff 10: # 在一定时间范围内找点对continuefreq_binned int(freq / upper_frequency * (2 ** frequency_bits))other_freq_binned int(other_freq / upper_frequency * (2 ** frequency_bits))hash int(freq_binned) | (int(other_freq_binned) 10) | (int(diff) 20)hashes[hash] (time, song_id)return hashes
特征提取feature_collect.py
# 获取数据库中所有音乐
path_music data
current_path os.getcwd()
path_songs os.path.join(current_path, path_music)
dic_idx2song song_collect(path_songs)# 对每条音乐进行特征提取
database {}
for song_id in dic_idx2song.keys():file dic_idx2song[song_id]print(collect info of file, file)# 读取音乐y, fs librosa.load(file, srfix_rate) # 提取特征对constellation_map collect_map(y, fs, win_length_secondswin_length_seconds, num_peaksnum_peaks)# 获取hash值hashes create_hash(constellation_map, fs, frequency_bitsfrequency_bits, song_idsong_id)# 把hash信息填充入数据库for hash, time_index_pair in hashes.items():if hash not in database:database[hash] []database[hash].append(time_index_pair)# 对数据进行保存
with open(database.pickle, wb) as db:pickle.dump(database, db, pickle.HIGHEST_PROTOCOL)
with open(song_index.pickle, wb) as songs:pickle.dump(dic_idx2song, songs, pickle.HIGHEST_PROTOCOL)
# 加载数据库
database pickle.load(open(database.pickle, rb))
dic_idx2song pickle.load(open(song_index.pickle, rb))
print(len(database))# 检索过程
def getscores(y, fs, database):# 对检索语音提取hashconstellation_map collect_map(y, fs)hashes create_hash(constellation_map, fs, frequency_bits10, song_idNone)# 获取与数据库中每首歌的hash匹配matches_per_song {}for hash, (sample_time, _) in hashes.items():if hash in database:maching_occurences database[hash]for source_time, song_index in maching_occurences:if song_index not in matches_per_song:matches_per_song[song_index] []matches_per_song[song_index].append((hash, sample_time, source_time))scores {}# 对于匹配的hash计算测试样本时间和数据库中样本时间的偏差for song_index, matches in matches_per_song.items():
# scores[song_index] len(matches)song_scores_by_offset {}# 对相同的时间偏差进行累计for hash, sample_time, source_time in matches:delta source_time - sample_timeif delta not in song_scores_by_offset:song_scores_by_offset[delta] 0song_scores_by_offset[delta] 1# 计算每条歌曲的最大累计偏差song_scores_by_offset sorted(song_scores_by_offset.items(), keylambda x: x[1], reverseTrue)scores[song_index] song_scores_by_offset[0]scores sorted(scores.items(), keylambda x:x[1][1], reverseTrue)return scores
音乐检索music_research.py
import threading
from playsound import playsounddef cycle(path):while 1:playsound(path)
def play(path, cycFalse):if cyc:cycle(path)else:playsound(path)path test_music/record4.wav
y, fs librosa.load(path, srfix_rate)
# 播放待检索音频
music threading.Thread(targetplay, args(path,))
music.start()# 检索打分
scores getscores(y, fs, database)# 打印检索信息
for k, v in scores:file dic_idx2song[k]name os.path.split(file)[-1]# print(%s :%d%(name, v))print(%s: %d: %d%(name, v[0], v[1]))# 打印结果
if len(scores) 0 and scores[0][1][1] 50:print(检索结果为:, os.path.split(dic_idx2song[scores[0][0]])[-1])
else:print(没有搜索到该音乐)
麦克风录音识别音乐
import pyaudio
import waveRATE 48000 # 采样率
CHUNK 1024 # 帧大小
record_seconds 10 # 录音时长s
CHANNWLS 2 # 通道数# 创建pyaudio流
audio pyaudio.PyAudio()stream audio.open(formatpyaudio.paInt16, # 使用量化位数16位channelsCHANNWLS, # 输入声道数目rateRATE, # 采样率inputTrue, # 打开输入流frames_per_bufferCHUNK) # 缓冲区大小frames [] # 存放录制的数据
# 开始录音
print(录音中。。。)
for i in range(0, int(RATE / CHUNK * record_seconds)):# 从麦克风读取数据流data stream.read(CHUNK)# 将数据追加到列表中frames.append(data)# 停止录音关闭输入流
stream.stop_stream()
stream.close()
audio.terminate()# 将录音数据写入wav文件中
with wave.open(test_music/test.wav, wb) as wf:wf.setnchannels(CHANNWLS)wf.setsampwidth(audio.get_sample_size(pyaudio.paInt16))wf.setframerate(RATE)wf.writeframes(b.join(frames))# 打开录音文件
path test_music/test.wav
y, fs librosa.load(path, srfix_rate)# 线程播放待检索音频
# music threading.Thread(targetplay, args(path,))
# music.start()# 音乐检索
print(检索中。。。)
scores getscores(y, fix_rate, database)# 打印检索信息
# for k, v in scores:
# file dic_idx2song[k]
# name os.path.split(file)[-1]
# # print(%s :%d%(name, v))
# print(%s: %d: %d%(name, v[0], v[1]))# 打印结果
if len(scores) 0 and scores[0][1][1] 50:print(检索结果为:, os.path.split(dic_idx2song[scores[0][0]])[-1])
else:print(没有搜索到该音乐) 参考音乐检索-Shazam算法原理_哔哩哔哩_bilibili
